Giovanni De Toni
Research Scientist - Mobile and Social Computing Lab
Human-centric and Responsible AI
Fondazione Bruno Kessler (FBK)


Hi 👋! I am Giovanni and I am currently a Research Scientist at Fondazione Bruno Kessler (FBK). My research interests relate to responsible and human-centric AI topics, focusing on counterfactual explanations, algorithmic recourse, causality and user-aware decision-making systems.

I am interested in studying human-centric machine learning systems - ensuring they can provably support human expertise (NeurIPS'24) while preserving human agency, especially by providing fail-safes (TMLR'24, FAccT'25) when things do not go as planned.

I hold a PhD from the University of Trento (cum laude), affiliated with the European Laboratory for Learning and Intelligent Systems (ELLIS). Throughout my academic journey, I conducted research as a visiting scientist or intern at several institutions, including the European Commission, Max Planck Institute for Software Systems, Google X, and CERN. Before my PhD, I was a Research Scientist at VUI, Inc., a Boston-based startup (now acquired) developing innovative conversational AI technologies. In the past, I have also contributed to several open-source scientific libraries.

Latest News

Publications & Preprints

  1. Time Can Invalidate Algorithmic Recourse
    Giovanni De Toni, Stefano Testo, Bruno Lepri, Andrea Passerini
    FAccT: ACM Conference on Fairness, Accountability, and Transparency (2025)
    [paper][code]

  2. Towards Human-AI Complementarity with Predictions Sets
    Giovanni De Toni, Nastaran Okati, Suhas Thejaswi, Eleni Straitouri, Manuel Gomez-Rodriguez
    NeurIPS (2024)
    [paper][code]

  3. Preference Elicitation in Interactive and User-centered Algorithmic Recourse: an Initial Exploration
    Seyedehdelaram Esfahani, Giovanni De Toni, Bruno Lepri, Andrea Passerini, Katya Tentori, Massimo Zancanaro
    ACM UMAP (2024)
    Best Short Paper Runner-up at the 32nd ACM UMAP Conference (2024)
    [paper][code (will be available soon)]

  4. Personalized Algorithmic Recourse with Preference Elicitation
    Giovanni De Toni, Paolo Viappiani, Stefano Teso, Bruno Lepri, Andrea Passerini
    Transactions on Machine Learning Research (2024)
    [paper][code]

  5. Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
    Giovanni De Toni, Bruno Lepri, Andrea Passerini
    Machine Learning (2023)
    [paper][code]

  6. Learning compositional programs with arguments and sampling
    Giovanni De Toni, Luca Erculiani, Andrea Passerini
    Advances in Programming Languages and Neurosymbolic Systems (AIPLANS), NeurIPS, 2021.
    10th International Workshop on Statistical Relational AI (StarAI), IJCLR, 2021.
    [paper][code][poster]

  7. A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data
    Giovanni De Toni, Cristian Consonni, Alberto Montresor
    PLOS ONE, 2021.
    [paper][code]

  8. Pyglmnet: Python implementation of elastic-net regularized generalized linear models
    Mainak Jas, Titipat Achakulvisut, Aid Idrizović, Daniel Acuna, Matthew Antalek, Vinicius Marques, Tommy Odland, Ravi Prakash Garg, Mayank Agrawal, Yu Umegaki, Peter Foley, Hugo Fernandes, Drew Harris, Beibin Li, Olivier Pieters, Scott Otterson, Giovanni De Toni, Chris Rodgers, Eva Dyer, Matti Hamalainen, Konrad Kording, Pavan Ramkumar
    Journal of Open Source Software (JOSS), 2020.
    [paper][code]

Teaching